CMOS-3D smart imager architectures for feature detection

This paper reports a multi-layered smart image sensor architecture for feature extraction based on detection of interest points. The architecture is conceived for 3-D integrated circuit technologies consisting of two layers (tiers) plus memory. The top tier includes sensing and processing circuitry aimed to perform Gaussian filtering and generate Gaussian pyramids in fully concurrent way. The circuitry in this tier operates in mixed-signal domain. It embeds in-pixel correlated double sampling, a switched-capacitor network for Gaussian pyramid generation, analog memories and a comparator for in-pixel analog-to-digital conversion. This tier can be further split into two for improved resolution; one containing the sensors and another containing a capacitor per sensor plus the mixed-signal processing circuitry. Regarding the bottom tier, it embeds digital circuitry entitled for the calculation of Harris, Hessian, and difference-of-Gaussian detectors. The overall system can hence be configured by the user to detect interest points by using the algorithm out of these three better suited to practical applications. The paper describes the different kind of algorithms featured and the circuitry employed at top and bottom tiers. The Gaussian pyramid is implemented with a switched-capacitor network in less than 50 μs, outperforming more conventional solutions.Xunta de Galicia 10PXIB206037PRMinisterio de Ciencia e Innovación TEC2009-12686, IPT-2011-1625-430000Office of Naval Research N00014111031

In-pixel generation of gaussian pyramid images by block reusing in 3D-CMOS

This paper introduces an architecture of a switched-capacitor network for Gaussian pyramid generation. Gaussian pyramids are used in modern scale- and rotation-invariant feature detectors or in visual attention. Our switched-capacitor architecture is conceived within the framework of a CMOS-3D-based vision system. As such, it is also used during the acquisition phase to perform analog storage and Correlated Double Sampling (CDS). The paper addresses mismatch, and switching errors like feedthrough and charge injection. The paper also gives an estimate of the area occupied by each pixel on the 130nm CMOS-3D technology by Tezzaron. The validity of our proposal is assessed through object detection in a scale- and rotation-invariant feature detector.Xunta de Galicia 10PXIB206037PRMinisterio de Ciencia e Innovación TEC2009-12686Office of Naval Research (USA) N00014111031

Hub of things: concentrador para el internet de las cosas

En la actualidad, cada vez encontramos más dispositivos electrónicos conectados a internet, monitoreados y controlados en forma remota. La diversidad tecnológica y la cantidad de dispositivos dificulta la integración de los mismos para su control, monitoreo e interacción. Las plataformas basadas en microcontroladores o de procesamiento reducido como Arduino, no brindan una conexión con niveles aceptables de seguridad. La privacidad constituye una dificultad, ya que los usuarios desconocen si los distintos proveedores de soluciones IoT utilizan sus datos o los venden a terceros. La latencia también es un problema. Muchos de los proveedores de soluciones en la nube no tienen servidores locales, lo que degrada el tiempo de reacción ante determinado evento. Se propone el Hub Of Things o HoT como una solución que integre localmente o en la nube, el control y monitoreo de los dispositivos. Proveerá además una interface de control, segura y homogénea, tanto gráfica como de programación. Será escalable porque contemplará un método que amplía la variedad de dispositivos a integrar y monitorear, posibilitando su interacción con otros sistemas. El HoT intentará solucionar los problemas de la diversidad tecnológica, por medio de una interface homogénea y segura. Sirviendo de mediador entre los dispositivos y el usuario.Eje: Arquitectura, Redes y Sistemas Operativos.Red de Universidades con Carreras en Informátic

Hub of things: concentrador para el internet de las cosas

En la actualidad, cada vez encontramos más dispositivos electrónicos conectados a internet, monitoreados y controlados en forma remota. La diversidad tecnológica y la cantidad de dispositivos dificulta la integración de los mismos para su control, monitoreo e interacción. Las plataformas basadas en microcontroladores o de procesamiento reducido como Arduino, no brindan una conexión con niveles aceptables de seguridad. La privacidad constituye una dificultad, ya que los usuarios desconocen si los distintos proveedores de soluciones IoT utilizan sus datos o los venden a terceros. La latencia también es un problema. Muchos de los proveedores de soluciones en la nube no tienen servidores locales, lo que degrada el tiempo de reacción ante determinado evento. Se propone el Hub Of Things o HoT como una solución que integre localmente o en la nube, el control y monitoreo de los dispositivos. Proveerá además una interface de control, segura y homogénea, tanto gráfica como de programación. Será escalable porque contemplará un método que amplía la variedad de dispositivos a integrar y monitorear, posibilitando su interacción con otros sistemas. El HoT intentará solucionar los problemas de la diversidad tecnológica, por medio de una interface homogénea y segura. Sirviendo de mediador entre los dispositivos y el usuario.Eje: Arquitectura, Redes y Sistemas Operativos.Red de Universidades con Carreras en Informátic

Hub of things: concentrador para el internet de las cosas

En la actualidad, cada vez encontramos más dispositivos electrónicos conectados a internet, monitoreados y controlados en forma remota. La diversidad tecnológica y la cantidad de dispositivos dificulta la integración de los mismos para su control, monitoreo e interacción. Las plataformas basadas en microcontroladores o de procesamiento reducido como Arduino, no brindan una conexión con niveles aceptables de seguridad. La privacidad constituye una dificultad, ya que los usuarios desconocen si los distintos proveedores de soluciones IoT utilizan sus datos o los venden a terceros. La latencia también es un problema. Muchos de los proveedores de soluciones en la nube no tienen servidores locales, lo que degrada el tiempo de reacción ante determinado evento. Se propone el Hub Of Things o HoT como una solución que integre localmente o en la nube, el control y monitoreo de los dispositivos. Proveerá además una interface de control, segura y homogénea, tanto gráfica como de programación. Será escalable porque contemplará un método que amplía la variedad de dispositivos a integrar y monitorear, posibilitando su interacción con otros sistemas. El HoT intentará solucionar los problemas de la diversidad tecnológica, por medio de una interface homogénea y segura. Sirviendo de mediador entre los dispositivos y el usuario.Eje: Arquitectura, Redes y Sistemas Operativos.Red de Universidades con Carreras en Informátic

Switched-capacitor networks for scale-space generation

In scale-space filtering signals are represented at several scales, each conveying different details of the original signal. Every new scale is the result of a smoothing operator on a former scale. In image processing, scale-space filtering is widely used in feature extractors as the Scale-Invariant Feature Transform (SIFT) algorithm. RC networks are posed as valid scale-space generators in focal-plane processing. Switched-capacitor networks are another alternative, as different topologies and switching rate offer a great flexibility. This work examines the parallel and the bilinear implementations as two different switched-capacitor network topologies for scale-space filtering. The paper assesses the validity of both topologies as scale-space generators in focal-plane processing through object detection with the SIFT algorithm.Xunta de Galicia 10PXI206037PRMinisterio de Ciencia e Innovación TEC2009- 12686, TEC2009-11812Office of Naval Research (USA) N00014111031

Gaussian Pyramid Extraction with a CMOS Vision Sensor

Comunicación presentada en 2014 14th International Workshop on Cellular Nanoscale Networks and Their Applications, CNNA 2014; University of Notre Dame; United States; 29 July 2014 through 31 July 2014This paper addresses a CMOS vision sensor with 176 × 120 pixels in standard 0.18 μm CMOS technology that computes the Gaussian pyramid. The Gaussian pyramid is extracted with a double-Euler switched-capacitor network, giving RMSE errors below 1.2% of full-scale value. The chip provides a Gaussian pyramid of 3 octaves with 6 scales each with an energy cost of 26.5 nJ at 2.64 Mpx/s.Gobierno de España ONR N000141410355 TEC2009-12686 MICINNMINECO TEC2012- 38921-C02 (FEDER)MINECO IPT-2011-1625-430000 IPC-20111009Junta de Andalucía TIC 2338-2013Xunta de Galicia EM2013 / 038 (FEDER)FEDER CN2012/151 GPC2013 / 04

Assessment of conjunctival, episcleral and scleral thickness in healthy individuals using anterior segment optical coherence tomography

Purpose: To determine the thickness of the conjunctiva, episclera and sclera in healthy individuals using anterior segment optical coherence tomography (AS-OCT).Methods: We prospectively included 107 healthy individuals of different age groups (18-39 years, 40-54 years, 55-69 years and >= 70 years). For each eye, AS-OCT scans of four quadrants (temporal, nasal, superior and inferior) were acquired. The thickness of the conjunctiva, episclera and sclera was measured for each scan. In addition, the axial length of both eyes was measured, and general characteristics, including smoking, allergies and contact lens use, were collected.Results: The mean conjunctival thickness was significantly different between the nasal and superior quadrants (87 +/- 30 mu m vs. 77 +/- 16 mu m; p < 0.001), as well as the superior and inferior quadrants (77 +/- 16 mu m vs. 86 +/- 19 mu m; p = 0.001). The mean episcleral thickness was larger in the superior (174 +/- 54 mu m) and inferior (141 +/- 43 mu m) quadrants, compared to the nasal (83 +/- 38 mu m) and temporal quadrants (90 +/- 44 mu m). The mean scleral thickness of the inferior quadrant was the largest (596 +/- 64 mu m), followed by the nasal (567 +/- 76 mu m), temporal (516 +/- 67 mu m) and superior (467 +/- 52 mu m) quadrants (all p < 0.001). The averaged scleral thickness increased 0.96 mu m per age year (0.41-1.47 mu m, p < 0.001).Conclusions: This study provides an assessment of the thickness of scleral and adjacent superficial layers in healthy individuals determined on AS-OCT, which could enable future research into the use of AS-OCT in diseases affecting the anterior eye wall

Low-Power CMOS Vision Sensor for Gaussian Pyramid Extraction

This paper introduces a CMOS vision sensor chip in a standard 0.18 μm CMOS technology for Gaussian pyramid extraction. The Gaussian pyramid provides computer vision algorithms with scale invariance, which permits having the same response regardless of the distance of the scene to the camera. The chip comprises 176×120 photosensors arranged into 88×60 processing elements (PEs). The Gaussian pyramid is generated with a double-Euler switched capacitor (SC) network. Every PE comprises four photodiodes, one 8 b single-slope analog-to-digital converter, one correlated double sampling circuit, and four state capacitors with their corresponding switches to implement the double-Euler SC network. Every PE occupies 44×44 μm2 . Measurements from the chip are presented to assess the accuracy of the generated Gaussian pyramid for visual tracking applications. Error levels are below 2% full-scale output, thus making the chip feasible for these applications. Also, energy cost is 26.5 nJ/px at 2.64 Mpx/s, thus outperforming conventional solutions of imager plus microprocessor unit.Office of Naval Research, USA N00014-14-1-0355Ministerio de Economía y Competitividad TEC2015-66878- C3-1-R, TEC2015-66878-C3-3-RJunta de Andalucía TIC 2338, EM2013/038, EM2014/01

Assessment of conjunctival, episcleral and scleral thickness in healthy individuals using anterior segment optical coherence tomography

Purpose: To determine the thickness of the conjunctiva, episclera and sclera in healthy individuals using anterior segment optical coherence tomography (AS-OCT).Methods: We prospectively included 107 healthy individuals of different age groups (18-39 years, 40-54 years, 55-69 years and >= 70 years). For each eye, AS-OCT scans of four quadrants (temporal, nasal, superior and inferior) were acquired. The thickness of the conjunctiva, episclera and sclera was measured for each scan. In addition, the axial length of both eyes was measured, and general characteristics, including smoking, allergies and contact lens use, were collected.Results: The mean conjunctival thickness was significantly different between the nasal and superior quadrants (87 +/- 30 mu m vs. 77 +/- 16 mu m; p < 0.001), as well as the superior and inferior quadrants (77 +/- 16 mu m vs. 86 +/- 19 mu m; p = 0.001). The mean episcleral thickness was larger in the superior (174 +/- 54 mu m) and inferior (141 +/- 43 mu m) quadrants, compared to the nasal (83 +/- 38 mu m) and temporal quadrants (90 +/- 44 mu m). The mean scleral thickness of the inferior quadrant was the largest (596 +/- 64 mu m), followed by the nasal (567 +/- 76 mu m), temporal (516 +/- 67 mu m) and superior (467 +/- 52 mu m) quadrants (all p < 0.001). The averaged scleral thickness increased 0.96 mu m per age year (0.41-1.47 mu m, p < 0.001).Conclusions: This study provides an assessment of the thickness of scleral and adjacent superficial layers in healthy individuals determined on AS-OCT, which could enable future research into the use of AS-OCT in diseases affecting the anterior eye wall